Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45–79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.

Automated machine learning (AutoML) tools empower organizations to unlock valuable new business insights, integrate advanced AI capabilities into applications, and enable both data scientists and non-technical experts to swiftly build predictive models. Complex models generated by AutoML can appear to operate as “black boxes.” This lack of transparency can be a major disadvantage in relation to compliance with the legislation (e.g., European Union regulations implementing a “right to explanation” of algorithmic decisions provided by artificial intelligence systems). Furthermore, in many applications one must not trust a black-box system by default. We approach the problem of confidence in models generated using AutoML frameworks in two areas: model explanation and model validation, respectively. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we propose a model-agnostic approach based on SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. We conducted a comparative analysis of three AutoML frameworks, examining their architectures and practical aspects, which aims at transparency in the generation of automatic learning models. Transparent model generation helps stakeholders understand how models are created, leading to greater confidence in their reliability and suitability for deployment in real-world scenarios.

The article aims to provide a perspective on economic growth by relying on the influence and use of the stock market as an economic lever. Two methods will be used: a quantitative one, determined by a multiple linear regression model, and a qualitative one that encumbers a sustainable vector model for generating economic growth. The data panel covers 36 states, for a period of 21 years. The paper manages to identify the main control functions that the stock exchange has over the macroeconomic context, through the quantitative and qualitative method, and to highlight the most important positive and negative attributes of using qualitative methods, in contrast to quantitative ones. The results show a predominant probabilistic characteristic of quantitative methods, in contrast to the flexibility and complexity of the qualitative method, which has been used. Additionally, the quantitative method offers a strictly cartesian perspective for determining future scenarios, while the sustainable vector model, based on a fractalized vision of reality, manages to capture a plurality of perspectives, as well as the interrelationships between the determining parameters, thus being a complex system of simple equations, as opposed to the quantitative method which is defined as a simple system of complex equations.

Workflows coordinate a series of computing tasks to create a sophisticated workflow logic. Ensuring the correctness of a workflow specification is essential for automating business processes. Errors in the specification should be identified and resolved as early as possible, during the design phase. In this paper, we propose a verification approach for workflow specifications utilizing model checking techniques. We introduce a method for verifying the correctness properties of workflow processes by utilizing our database-embedded Alternating-time Temporal Logic (ATL) model checker. First, the workflow specification is translated into a concurrent game structure (CGS). Next, the desired property is expressed as an ATL formula. Finally, the ATL model checker is executed to verify whether the correctness properties hold for the model. To support users in the formalization of business constraints, the proposed solution implements an assistant based on generative AI. The experimental results show that employing the chain-of-thought prompting method significantly enhances the reasoning process performance when using the GPT-4o model.